[Shrinked]
Input noise is one type of instance structure to which we know that the learned models should be
invariant. Very often and depending on the type of application we have additional prior knowledge on the
instance structures to which the models should be invariant. In such cases a typical approach is to generate
training instances that exhibit such structures and let the model learn the invariances. For example for images
such corruption structures include translations, rotations, scalings,etc, [1]}, [2]}.

[Alexandros]I think we are missing a lot here, i.e. on what kind of invariances people have sought
to model through data augmentation or analytically. See for example the references in [3]} section 4.2,
we should find more recent stuff in which we generate instances with specific structure.
